کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
404000 677380 2014 8 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Feature selection and multi-kernel learning for sparse representation on a manifold
ترجمه فارسی عنوان
انتخاب ویژگی ها و یادگیری چند هسته ای برای نمایندگی نادر در یک منیفولد
کلمات کلیدی
نمایندگی داده ها، برنامه نویسی انعطاف پذیر، مانیفولد، انتخاب ویژگی، یادگیری چند هسته ای
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Sparse representation has been widely studied as a part-based data representation method and applied in many scientific and engineering fields, such as bioinformatics and medical imaging. It seeks to represent a data sample as a sparse linear combination of some basic items in a dictionary. Gao et al. (2013) recently proposed Laplacian sparse coding by regularizing the sparse codes with an affinity graph. However, due to the noisy features and nonlinear distribution of the data samples, the affinity graph constructed directly from the original feature space is not necessarily a reliable reflection of the intrinsic manifold of the data samples. To overcome this problem, we integrate feature selection and multiple kernel learning into the sparse coding on the manifold. To this end, unified objectives are defined for feature selection, multiple kernel learning, sparse coding, and graph regularization. By optimizing the objective functions iteratively, we develop novel data representation algorithms with feature selection and multiple kernel learning respectively. Experimental results on two challenging tasks, N-linked glycosylation prediction and mammogram retrieval, demonstrate that the proposed algorithms outperform the traditional sparse coding methods.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 51, March 2014, Pages 9–16
نویسندگان
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